An Integrative Approach for In Silico Glioma Research

• Published in: IEEE transactions on biomedical engineering Vol. 57; no. 10; pp. 2617 - 2621
• Main Authors: Cooper, Lee A. D., Flanders, Adam E., Rubin, Daniel L., Meir, Erwin G. Van, Kurc, Tahsin M., Moreno, Carlos S., Brat, Daniel J., Saltz, Joel H., Kong, Jun, Gutman, David A., Wang, Fusheng, Cholleti, Sharath R., Pan, Tony C., Widener, Patrick M., Sharma, Ashish, Mikkelsen, Tom
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bioinformatics; Biology; Biomedical informatics; brain tumor; Cancer; Cell Nucleus - pathology; Computational Biology - methods; Computer Simulation; Databases, Factual; Diseases; Genomics; Glioma - pathology; Humans; Image analysis; Image Processing, Computer-Assisted - methods; Immunohistochemistry; in silico; Magnetic resonance imaging; microscopy; Morphology; Neoplasms; Pathology
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2010.2060338

The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors, where the morphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.